mathcoreml 0.1.0

Mathematical Implementation of ML algorithms from scratch

MathCoreML

Lightweight utilities and example models for small-scale machine learning experiments.

Overview

MathCoreML is a compact Python library designed for learning, teaching, and rapid prototyping of small machine learning workflows.
The project emphasizes clarity, simplicity, and minimal dependencies, making it suitable for educational use and lightweight experiments.

It provides:

• Simple CSV data exploration utilities
• Basic data cleaning helpers
• Minimal reference implementations of classic ML models

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Features

📊 Data Utilities

• CSVStore
  • Easy CSV loading
  • Basic statistics (min, max, mean, counts)
  • Quick summaries and simple visualizations
• CleanData
  • IQR-based outlier detection
  • Lightweight data-cleaning helpers

🤖 Models

• Linear Regression
• Logistic Regression

These implementations are intentionally minimal and readable, aimed at education and experimentation rather than production-scale systems.

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Installation

Install from PyPI (once published):

pip install mathcoreml

For local development:

python -m venv .venv
source .venv/bin/activate
pip install -e .

Quick Start

Example usage with CSVStore (provide your own CSV file):

from MathCoreML.utils.CSVStore import CSVStore
from MathCoreML.utils.CleanData import CleanData

store = CSVStore('/absolute/path/to/your_dataset.csv')
print('Rows:', len(store))
print('Max Age:', store.max_of('Age'))

store.quick_summary()

Package Structure

MathCoreML/
├── src/
│   └── mathcoreml/
│       ├── Models/
│       │   ├── linearRegression.py
│       │   └── logisticRegression.py
│       └── utils/
│           ├── csvstore.py
│           ├── cleandata.py
│           └── modelevaluator.py
└── pyproject.toml

Notes

Designed for clarity and learning, not as a replacement for full ML frameworks.

Public API is limited to modules inside MathCoreML.

License

This project is licensed under the MIT License. See the LICENSE file for details.